Passive clear air turbulence detection system and method

ABSTRACT

A passive thermal imaging system is described. The system includes at least one detector array configured to detect thermal electromagnetic radiation (EMR), imaging optics, and processing electronics. The imaging optics are configured to receive thermal EMR from an object, and to image the received thermal EMR onto pixels of each of the at least one detector array. The processing electronics are configured to receive a detected signal from each of the pixels of the at least one detector array, to calculate a correlation value based on a correlation between the received detected signals from the pixels, and to compare the correlation value with a threshold correlation value to determine whether a detection event has occurred.

BACKGROUND OF THE INVENTION

Clear air turbulence (CAT) is the turbulent movement of air masses inthe absence of any visual cues such as clouds, and is caused when bodiesof air moving at widely different speeds meet. The atmospheric regionmost susceptible to CAT is the high troposphere at altitudes of around7,000-12,000 meters (23,000-39,000 ft) as it meets the tropopause. HereCAT is most frequently encountered in the regions of jet streams. Atlower altitudes it may also occur near mountain ranges. Thin cirrusclouds can also indicate a high probability of CAT.

CAT can be hazardous to the comfort, and even safety, of air travel. Thethermal characteristics of CAT are known. Studies show that gustvelocity changes in CAT of at least 20 ft sec⁻¹ are associated withtemperature changes of 3° C. or higher; very few being less than 1° C.Such studies show that CAT horizontal temperature gradients with aminimum temperature change of 2° C., and at a rate which equaled orexceeded 0.5° C. per minute. Moderately choppy CAT was observed at a 5°C. temperature change.

Conventionally, CAT has been measured using active electro-opticalheterodyne laser velocimeter systems at ranges exceeding 10 km. Suchactive systems typically use 10 micron wavelength LWIR (long wavelengthinfrared) CO₂ lasers, larger germanium optics and heterodyning optics.Fast, complex signal and data processing renders systems constructedalong these lines are expensive, power-hungry, heavy, and physicallylarge. Further such active systems require much maintenance on ause-by-use basis in alignment, cleaning etc.

SUMMARY OF THE INVENTION

According to one embodiment, there is provided a passive thermal imagingsystem, comprising: at least one detector array configured to detectthermal electromagnetic radiation (EMR); imaging optics configured toreceive thermal EMR from an object, and to image the received thermalEMR onto pixels of each of the at least one detector array; andprocessing electronics configured to receive a detected signal from eachof the pixels of the at least one detector array, to calculate acorrelation value based on a correlation between the received detectedsignals from the pixels, and to compare the correlation value with athreshold correlation value to determine whether a detection event hasoccurred.

According to one aspect of the embodiment, the at least one detectorarray comprises a single detector array, and the processing electronicsis configured to calculate a correlation value based on auto-correlationof the pixels of the single detector array.

According to another aspect of the embodiment, the at least one detectorarray comprises a plurality of detector arrays, and the processingelectronics is configured to calculate a correlation value based onmulti-correlation of corresponding pixels of the plurality of detectorarrays.

According to another aspect of the embodiment, the plurality of detectorarrays comprises two detector arrays, and the processing electronics isconfigured to calculate a correlation value based on cross-correlationof corresponding pixels of the plurality of detector arrays.

According to another aspect of the embodiment, the plurality of detectorarrays comprises three detector arrays, and the processing electronicsis configured to calculate a correlation value based ontriple-correlation of corresponding pixels of the plurality of detectorarrays.

According to another aspect of the embodiment, the at least one detectorarray comprises at least one of a nanoparticle plasmonic detector array,a mercury cadmium telluride detection array, or a bolometer detectorarray.

According to another aspect of the embodiment, the at least one detectorarray comprises a nanoparticle plasmonic detector array.

According to another aspect of the embodiment, the imaging optics areconfigured to receive thermal EMR from a region of the atmosphere as theobject, and where the detection event is clear air turbulence.

According to another aspect of the embodiment, the processingelectronics is configured to time integrate or spatially integrate thedetected signals at a rate of 1 to 10 times per second.

According to another aspect of the embodiment, the imaging opticscomprises an imaging lens.

According to another aspect of the embodiment, the passive thermalimaging system further comprises a bandpass filter which filters thereceived thermal EMR from the object within a EMR wavelength range.

According to another aspect of the embodiment, a system for detectingclear air turbulence, comprises: a structure having the passive thermalimaging system mounted thereon.

According to another aspect of the embodiment, the structure is one of avehicle or a ground-based platform.

According to another aspect of the embodiment, the structure is avehicle, which is one of an aircraft, a spacecraft, or an unmannedaerial vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustrating a passive thermal imaging systemaccording to an embodiment of the invention.

FIG. 2 is a schematic illustrating a view of a detector array includingan array of pixels according to an embodiment of the invention.

FIGS. 3A and 3B illustrate the effect of turbulence on EMR radiationreceived from a region of interest and impinging on a receiver plane.

FIG. 4 is graph illustrating standard deviation in arrival angle due toreasonable atmospheric turbulence level as a function of beam diameterd_(B) for different sizes of index of refraction fluctuation due toturbulence.

FIG. 5 illustrates a randomly populated signal matrix.

FIG. 6 illustrates a noise matrix.

FIG. 7 illustrates a signal plus noise matrix.

FIG. 8 is a schematic illustrating a system for detecting air turbulenceincluding a passive thermal imaging system according to an embodiment ofthe invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

According to certain embodiments of the present invention, a passiveoptical system which may discriminate a minimum change of 2° C. from animaged region and its background, and thus is appropriate for detectingCAT is described. The passive system uses correlation techniques toreduce the effects of thermal background noise to allow for detection ofCAT at a distance from the system of >10 km. Such a detection distanceof CAT provides a warning time of about 30 seconds for an optical systemon an aircraft traveling at about 600 miles/hr. Such a small temperaturechange may be determined in the presence of certain natural backgroundradiation from the day time and night time sky, although not necessarilyin the presence of all strengths of natural background radiation.

FIG. 1 illustrates a passive thermal imaging system 100 according to anembodiment of the invention. The system 100 includes at least onedetector array 110, (110 a and 110 b in FIG. 1), imaging optics 120(imaging optics components 120 a and 120 b in FIG. 1), and processingelectronics 130. The imaging optics 120 images an object of interest 150onto the at least one detector array 110. While FIG. 1 illustrates theat least detector array 110 to be two detector arrays 110 a and 110 b,the at least one detector array 110 may be a single detector array, ormore than two detector arrays, such as three detector arrays. Likewise,while FIG. 1 illustrates the imaging optics to be two imaging opticscomponents 120 a and 120 b, the imaging optics 120 may be a singleimaging optics component, or more than two imaging optics components,such as three imaging optics components. In general, the imaging opticscomponents 120 a and 120 b image thermal electromagnetic radiation (EMR)from an object 150 of interest onto detector arrays 110 a and 110 b,respectively.

FIG. 2 illustrates a view of a detector array 110 including an array ofpixels 112. While FIG. 2 illustrates a 3×3 array of pixels for ease ofillustration, in general, the size of the array of pixels will be muchlarger than 3×3. The detector material for the pixels 112 may be anymaterial appropriate for detecting thermal EMR at an appropriatewavelength. In this regard, the detector array 110 may comprise at leastone of a nanoparticle plasmonic detector array, a mercury cadmiumtelluride detection array, or a bolometer detector array. Nanoparticleplasmonic detector arrays are described in, for example, U.S. patentapplication Ser. No. 13/243,342 entitled NANO-STRUCTURE ARRAYS FOR EMRIMAGING, filed Sep. 23, 2011, which is incorporated by reference in itsentirely herein.

Referring back to FIG. 1, the system 100 may include at least onebandpass filter 140 (filters 140 a and 140 b in FIG. 1) which filtersthe received thermal EMR from the object 150 within a EMR wavelengthrange of interest. For example, for a EMR wavelength of interest of 10microns, the bandpass filter 140 may pass EMR in a wavelength range of10 microns±2 microns.

The imaging optics 120 images the EMR from the object at a desiredwavelength of interest. For example, for a EMR wavelength of interest of10 microns, the imaging optics may comprise a lens, or lenses, made ofgermanium to image the thermal EMR from the object 150.

The imaging system 100 may be of appropriate dimensions for imagingthermal EMR from an object at an appropriate distance. For example, ifthe system 100 is intended to image thermal EMR from an object at adistance of about 10 km from the system 100, the system may be an f/5system, for example, where the imaging optics 120 has a focal length ofabout 0.5 meters, for example, and a lens diameter of about 10 cm, forexample.

The processing electronics 130 receives a detected signal from each ofthe pixels 112 of the at least one detector array 110. The processingelectronics 130 further calculates a correlation value based on acorrelation between the received detected signals from the pixels 112,and compares the correlation value with a threshold correlation value todetermine whether a detection event has occurred.

Below is provided a background discussion for determining the signal tonoise and event detection capability of the system 100, where the eventis detection of CAT.

Basic Optical Principles for System and Signal Strength

For an extended source that fills the field of view of a detector, thedetector irradiance H is related to the source radiance, N, by thefollowing radiometric equation, where trans is the transmission of theatmosphere and system optics, m is the system magnification, v/u, andFN₀ is the f/# of the system:

$:={\pi \cdot N \cdot \frac{trans}{4 \cdot {FNo}^{2} \cdot \left( {m + 1} \right)^{2}}}$

For a 273° K object temperature, the spectral radiance of the extendedbody, where emissivity is assumed to be equal to unity, is ˜8 Watts persquare meter, steradian, micron. For the above 0.5 m, f/5 lens, operatedwith an 8-12 micron bandpass filter, and with 50% atmospheric andoptical transmission efficiency overall, the detector irradiance H iscalculated to be ˜0.5 Watts per square meter.

For a 10 micron square nanoparticle plasmonic detector array operated inthe LWIR region of 8-12 microns, where such a nanoparticle plasmonicdetector array is described in, for example, U.S. patent applicationSer. No. 13/243,342 entitled NANO-STRUCTURE ARRAYS FOR EMR IMAGING,filed Sep. 23, 2011, which is incorporated by referenced in its entirelyherein, the maximum Responsivity may be estimated to be about 5000Amps/Watt, with a RMS Noise performance at 2 pico-Amps. Presuming aResponsivity in practice to be about 500 Amps/Watt, the signal to noiseratio in such a nanoparticle plasmonic pixel would be ˜1.4×10⁴, and atleast 1,000 even if the detector noise was 10× greater. Alternatively, atypical MCT detector of a 15 micron pixel side cooled to 77° K wouldyield a signal to noise ratio of ˜40, and an un-cooled typicalmicrobolometer of a 17 micron pixel side would yield a signal to noiseratio of ˜9.5.

From a system performance point of view, of concern is the measurementof the difference in temperature of the target object from its adjacentbackground, which should be about 2° C. for CAT detection. In measuringsuch a 2° C. temperature difference, the Minimum Resolvable TemperatureDifference (MRTD) and Minimum Detectable Temperature Difference (MDTD)are the parameters of importance as is known in thermal imaging. Todetermine the MRTD, NDTD, as well as Noise Equivalent TemperatureDifference (NETD), standard equations may be used as in known [Lloyd, J.M., 1975; ‘Thermal Imaging Systems’, Plenum Press].

In calculating the MRTD and MDTD, a dwell time of 0.2 seconds ispresumed. The NETD for the three detectors under consideration notedabove is determined to be ˜11 milliKelvin for a nanoparticle plasmonicdetector, ˜96 milliKelvin for a typical MCT detector, and ˜980milliKelvin for a typical microbolometer detector.

The MDTD may be calculated for a 1 kHz bandwidth system, which providesfor the three detectors under consideration noted above as follows: ˜4.6milliKelvin for a nanoparticle plasmonic detector, ˜24 milliKelvin for atypical MCT detector, and ˜300 milliKelvin for a typical microbolometerdetector.

Thus, without accounting for natural background radiation, all threeLWIR detectors noted can measure the necessary temperature differencerequired for CAT detection.

Background Radiation, Atmospheric Transmission, Turbulence Effects

In practice, however, the natural background radiation, atmospherictransmission and turbulence effects must be taken into account indetermining whether or not CAT may be detected. The ability todiscriminate against background noise contributions and fluctuations isof critical importance to effective realization in practice of theconcept of passive CAT discrimination and reliable measurement.

Background noise can enter an optical system for detection of CAT from awide range of circumstances, such as looking at the sun, the moon as thebackground, looking at clouds, or the day or night sky, or even at theEarth itself. For the purposes of performance calculations, themagnitude of the different background noise contributions that might beencountered by a CAT system in practice must be considered, where suchcontributions may come from the, sun, the daytime sky, the full moon,the earth, or the brightest stars.

Further, atmospheric transmission must be taken into account indetermining whether or not CAT may be detected. The transmittance of theatmosphere at an EMR wavelength of 10 microns is of concern for thesystem described above. The overall transmissivity of the atmosphere,per km, is about 80% per km for a wavelength region of 10 microns.

The turbulence of interest is associated with small thermalfluctuations, which along a 10 km path length, may have an appreciableeffect on the integrity of the image. In order to estimate the effect ofturbulence on the image, information on the thermal fluctuations likelyat 10 micron EMR wavelength is needed. The effect of turbulence can beexplained with respect to FIGS. 3A, 3B and 4.

FIGS. 3A and 3B illustrate the effect of turbulence on EMR radiationreceived from a region of interest located at the point “Transmitter”and impinging on a receiver plane. The turbulence will cause fluctuationin the index of refraction of the air, thus affecting the imaging ontothe image plane. The effect of atmospheric turbulence depends on therelative sizes of the beam diameter, d_(B), and the size of thefluctuation, 1. FIG. 3A illustrates the case where the size of thefluctuation 1 is much less than the beam diameter d_(B) of the radiationfrom the point “Transmitter,” while FIG. 3B illustrates the case wherethe size of the fluctuation 1 is much greater than the beam diameterd_(B) of the radiation from the point “Transmitter.” As seen in FIGS. 3Aand 3B, if d_(B)/1<<1, the major effect of turbulence is to deflect theimaging-beam as a whole. If d_(B)/1<<1, small portions of the beam arediffracted and the imaging beam can become badly distorted.

FIG. 4 illustrates standard deviation in arrival angle due to reasonableatmospheric turbulence level as a function of beam diameter d_(B) fordifferent sizes of index of refraction fluctuation due to turbulence[see W. K. Pratt, (1969), Laser Communications Systems, Wiley].

Basic Performance Estimates

Expressions for the background radiation power at the detector arederived from the standard radiometry equation found in many opticstextbooks [see Pratt, W. K., Laser Communication Systems, Wiley (1969)].These expressions are summarized in the table below.

Background Source Radiation Relationship Expression Quantity Any source$P_{B} = {\frac{\pi\;\tau_{a}\tau_{r}\lambda_{i}d_{r}^{2}}{4}{\mathcal{W}(\lambda)}}$Spectral irradiance Spherical source of diameter, d_(s),$P_{B} = {\frac{\pi\;\tau_{a}\tau_{r}\lambda_{i}d_{r}^{2}}{16\; R^{2}}{\mathcal{W}(\lambda)}}$Spectral radiant emittance not filling receiver field of view$P_{B} = {\frac{{\pi\;}^{2}\tau_{a}\tau_{r}\lambda_{i}d_{r}^{2}}{16\; R^{2}}{\mathcal{N}(\lambda)}}$Spectral radiance Photon$P_{B} = {\frac{{\pi\;}^{2}\tau_{a}\tau_{r}\lambda_{i}d_{r}^{2}h\; f_{c}}{16\; R^{2}}{\mathcal{Q}(\lambda)}}$spectral radiance Extended source filling receiver$P_{B} = {\frac{\pi\;\tau_{a}\tau_{r}\lambda_{i}\theta_{R}^{2}d_{r}^{2}}{4}{\mathcal{W}(\lambda)}}$Spectral radiant emittance field of view, θ_(R)$P_{B} = {\frac{{\pi\;}^{2}\tau_{a}\tau_{r}\lambda_{i}\theta_{R}^{2}d_{r}^{2}}{4}{\mathcal{N}(\lambda)}}$Spectral radiance Photon$P_{B} = {\frac{{\pi\;}^{2}\tau_{a}\tau_{r}\lambda_{i}\theta_{R}^{2}d_{r}^{2}h\; f_{c}}{4}{\mathcal{Q}(\lambda)}}$spectral radiance,where the parameters shown in the table are as follows:

-   -   τ_(a) atmospheric transmissivity    -   τ_(r) receiver transmissivity    -   λ_(i) input filter bandwidth in wavelength units (X is        wavelength)    -   θ_(R) receiver field-of-view angle    -   d_(s) diameter of the background radiation source    -   d_(r) diameter of the receiver    -   H spectral irradiance    -   N spectral radiance    -   W spectral radiant emittance in wavelength units    -   P_(B) background radiation average power at the detector surface    -   R Range

At 10 microns wavelength, the values of background due to sun, daytimesky, night-time sky, full moon, earth and brightest stars are asfollows:

Sun: H(λ)~10⁻⁵ Watts per cm². Daytime Sky: N(λ)~5 × 10⁻⁴ Watts per cm²,micron, steradian. Night-time Sky: N(λ)~0.1 ×10⁻¹⁰ Watts per cm²,micron, steradian. Full Moon: H(λ)~10⁻¹⁰ Watts per cm², micron. Earth:W(λ)~3 × 10⁻³ Watts per cm², micron. Brightest Stars: H(λ)~10⁻¹⁴ Wattsper cm², micron.

Based on these values, the background power at a pixel in our opticalsystem may be calculated. For a pixel side being 10 microns, and thefocal-length of the lens being set, as above, at 0.5 meters, thefollowing background power levels at the detector pixel, under thebackground conditions may be calculated to be:

Sun: ~1.3 × 10⁻³ Watts. Daytime Sky: ~8 × 10⁻¹¹ Watts. Night-time Sky:~1.6 × 10⁻¹² Watts. Full Moon: ~1.3 × 10⁻⁸ Watts. Earth: ~4.7 × 10⁻¹⁰Watts. Brightest Stars: negligible.

By applying the Responsivity (Amps/Watt) to this natural backgroundnoise power, the induced natural background noise current may becalculated and the signal to noise ratio may be estimated (neglectingatmospheric transmission for a worst case calculation). The signal tonoise with no natural background noise, and for natural background noisedue to daytime sky are estimated as shown in the table below for ananoparticle plasmonic detector, a typical MCT detector, and a typicalmicrobolometer detector, for responsivity (Resp) values as shown.

SNR: No SNR: Daytime background Sky Background nanoparticle plasmonic_((Resp=500)) ~10⁴ 0.63 MCT _((Resp=120)) ~40  0.57 Microbolometer_((Resp=500)) ~95  0.15

As can be seen, even though the nanoparticle plasmonic detector hasextremely low noise compared to both the MCT and microbolometerdetectors, the magnitude of the natural background daylight sky noisedominates the detector noise itself.

Detector Array Correlation Signal Processing for Passive CAT

As noted above, the passive system uses correlation techniques to reducethe effects of thermal background noise to allow for detection of CAT ata distance from the system of ≥10 km. The type of correlation techniquesmay depend on the number of detector arrays employed in the passivesystem. Returning to FIG. 1, if the detector array 110 comprises asingle detector array, the processing electronics 130 may calculate acorrelation value based on auto-correlation of the pixels of the singledetector array. If the detector array 110 comprises a plurality ofdetector arrays, the processing electronics 130 may calculate acorrelation value based on multi-correlation of corresponding pixels ofthe plurality of detector arrays. For example, if the detector array 110comprises two detector arrays, such as the detector arrays 110 a and 110b shown in FIG. 1, the processing electronics 130 may calculate acorrelation value based on a cross-correlation of corresponding pixelsof the two detector arrays.

An example of a correlation technique for the detector array 110comprising two detector arrays is now described. Each of the detectorarrays 110 a and 110 b are arranged to image an overlapping, though notidentical region in space.

The correlation coefficient ρ_(X,Y) between two random variables X and Yhaving standard deviations σ_(X) and σ_(Y) is defined as:

$\rho_{X,Y} = {{{corr}\left( {X,Y} \right)} = \frac{{cov}\left( {X,Y} \right)}{\sigma_{X}\sigma_{Y}}}$where corr(X,Y) is the correlation function, nd cov(X,Y) is thecovariance function.

For two detector matrix arrays A and B, the covariance of the elementsin the m by n arrays A and B is defined as:

${c\;{{var}\left( {A,B} \right)}} = {\frac{1}{mn}{\sum\limits_{i = 0}^{m - 1}{\sum\limits_{j = 0}^{n - 1}{\left\lbrack {A_{i,j} - {{mean}(A)}} \right\rbrack\overset{\_}{\left\lbrack {B_{i,j} - {{mean}(B)}} \right\rbrack}}}}}$where A_(i,j) and B_(i,j) are the i, jth elements of the arrays A and B,respectively, and the bar indicates complex conjugation, and

${{mean}(A)} = {\frac{1}{mn}{\sum\limits_{i = 0}^{m - 1}{\sum\limits_{j = 0}^{n - 1}A_{i,j}}}}$

The correlation function of the two detector arrays can be calculated bythe processing electronics 130 based on the above equation for thecovariance of the elements.

For small array sizes, perhaps 100² elements, the correlation value,Corr (A, B), rapidly computes a scalar between 0 (0%) and 1 (100%) asthe correlation value; i.e., Pearson's r coefficient.

The processing electronics may then compare the value of the correlationvalue with a threshold value, which may be between 0.8 and 0.85 forexample, and if the correlation value is above the threshold value, theprocessing electronics indicates that an event has occurred, where theevent may be the existence of CAT.

The processing electronics may calculate the correlation coefficientmany times per second with dedicated fast logic (>5 times per seconddwell/integration time), to provide a continuous stream of correlationvalues that could be compared to a threshold level, above which a highprobability of CAT signals is expected to have been the cause. Forexample, the processing electronics may calculate the correlationcoefficient 1 to 10 times per second.

While a cross-correlation coefficient is calculated for two detectorarrays, alternatively the auto-correlation coefficient may be calculatedfor a single detector array, or a triple-correlation coefficient may becalculated for a three detector array arrangement.

Correlation Performance Estimates for a Two Detector Array Passive CATScheme

Below is described a simulation for estimating the correlationperformance for a two detector array arrangement. From the calculationabove regarding signal to noise ratios in the presence of daytime sky,the RMS noise level is taken to be about 0.5. In this simulation thereare two sets of 10×10 matrix elements, corresponding to a 10×10arrangement of pixels in two detector arrays, with numbers for the noisevalue taken from a random distribution of range 0 to 1.

In this simulation, the peak signal levels, representing CATfluctuations of around 2° C. will take values from 0.5 (same as the rmsnoise) to 2, and will only populate 20% of the sensor array elements, towhich the noise will also be added.

As a simulation, for the 20% of elements, the randomly populated signalmatrix is taken as is shown in FIG. 5, where L is the signal level foreach pixel having the signal level L, while the remaining 80% of theelements have a 0 signal. This simulation corresponds to the situationwhere a “turbulent cluster” is mostly imaged in the top left hand cornerof the signal matrix. The signal matrix for the second detector arrayhas similar but not the same spatial characteristics as that for thefirst detector array.

For the noise backgrounds, two matrices, one for each of the signalmatrices, are constructed with random numbers as described, where one ofthe noise matrices, the one of the first detector array, is shown inFIG. 6.

Setting the signal level L at 1.0, the signal plus noise matrix has thetypical form as shown in FIG. 7. The correlation value, Corr (A, B), maybe determined based on the signal plus noise matrices of the twodetector arrays according to the above equations.

The table below illustrates what happens to the correlation coefficientas the value of the signal level L of the 20% filled signal pixels israised from an RMS noise value of 0.5 up to 2.0.

L Correlation value 0.5 0.36 1.0 0.7 1.5 0.84 2.0 0.9 2.5 0.94 3.0 0.964.0 0.97

A correlation threshold value in the region of 0.8 to 0.85 allows forthe beginning of detection of a fairly low and sparsely populateddetector element against a similar level of RMS noise background.

Higher correlation values would be expected to be achieved for largersizes, such as for 100×100 or 1000×1000, presuming the correlationvalues can be correlated in real time.

Mounted Optical System

As illustrated in FIG. 8, the above described thermal imaging system maybe mounted in practice on an appropriate platform. FIG. 8 illustrates asystem 800 for detecting air turbulence. The system 800 comprises astructure 810, and a passive thermal imaging system 100, such as thatdescribed with respect to FIG. 1, mounted on the structure 800. As canbe seen in FIG. 8, the detector arrays 110 a and 110 b may be arrangedin separated regions of the structure 810, and to image the same object150.

The structure 800 may be a vehicle or a ground based platform. Thevehicle may be an aircraft, a spacecraft, or an unmanned aerial vehicle(UAV), for example.

The embodiments of the invention has been described in detail withparticular reference to preferred embodiments thereof, but it will beunderstood by those skilled in the art that variations and modificationscan be effected within the spirit and scope of the invention.

What is claimed is:
 1. A passive thermal imaging system, comprising: aplurality of detector arrays, each of the detector arrays configured todetect thermal electromagnetic radiation (EMR) within a same band arounda desired EMR wavelength, each of the detector arrays comprising aplurality of pixels; imaging optics configured to receive thermal EMRwithin the band from an object, and to image the received thermal EMRfrom a same region of the object onto pixels of each of the plurality ofdetector arrays; and processing electronics configured to receive adetected signal from each of the pixels of the plurality of detectorarrays, to calculate a correlation value based on a multi-correlation ofthe received detected signals of corresponding pixels of differentdetector arrays of the plurality of detector arrays, the detectedsignals based on the thermal EMR from the object, and to compare thecorrelation value with a threshold correlation value to determine that adetection event has occurred in response to the correlation valueexceeding the threshold correlation value being equal to or between 0.8and 0.85, the processing electronics are configured to calculate thecorrelation value based on a covariance of the received detected signalsof the corresponding pixels of the different detector arrays, thedetection event corresponding to a size of a temperature fluctuation ofthe object, wherein the imaging optics are configured to receive thermalEMR from a region of the atmosphere as the object, and where thedetection event is clear air turbulence exhibiting thermal fluctuationsof air at a distance from the system ≥10 kilometers.
 2. The passivethermal imaging system of claim 1, wherein the plurality of detectorarrays comprises two detector arrays, and the processing electronics isconfigured to calculate a correlation value based on cross-correlationof corresponding pixels of the plurality of detector arrays.
 3. Thepassive thermal imaging system of claim 1, wherein the plurality ofdetector arrays comprises three detector arrays, and the processingelectronics is configured to calculate a correlation value based ontriple-correlation of corresponding pixels of the plurality of detectorarrays.
 4. The passive thermal imaging system of claim 1, wherein theplurality of detector arrays comprise at least one of a nanoparticleplasmonic detector array, a mercury cadmium telluride detection array,or a bolometer detector array.
 5. The passive thermal imaging system ofclaim 4, wherein the plurality of detector arrays comprise ananoparticle plasmonic detector array.
 6. The passive thermal imagingsystem of claim 1, wherein the processing electronics is configured totime integrate or spatially integrate the detected signals at a rate of1 to 10 times per second.
 7. The passive thermal imaging system of claim1, wherein the imaging optics comprises an imaging lens.
 8. The passivethermal imaging system of claim 7, further comprising: a bandpass filterwhich filters the received thermal EMR from the object within a EMRwavelength range.
 9. A system for detecting clear air turbulence,comprising: a structure having the passive thermal imaging system ofclaim 1 mounted thereon.
 10. The system of claim 9, wherein thestructure is one of a vehicle or a ground-based platform.
 11. The systemof claim 10, wherein the structure is a vehicle, which is one of anaircraft, a spacecraft, or an unmanned aerial vehicle.
 12. The passivethermal imaging system of claim 1, further comprising: a bandpass filterwhich filters the received thermal EMR from the object within a EMRwavelength range of 10 microns±2 microns.